Appendix 36 Moving towards a better understanding of airborne transmission of FMD John Gloster1*, Isabel Esteves 2 and Soren Alexandersen 1
3
Met Office, UK, based at Pirbright Laboratory, Institute for Animal Health, Ash Rd, Woking, Surrey, GU24 0NF, UK 2 Pirbright Laboratory, Institute for Animal Health, Ash Rd, Woking, Surrey, GU24 0NF, UK 3 Danish Institute for Food and Veterinary Research, Department of Virology, Lindholm, DK-4771 Kalvehave, Denmark
Abstract In the event of an outbreak of FMD it is essential for those responsible for controlling and eradicating the disease to quickly assess how the initial animals became infected and its potential for further spread. Once this has been determined the appropriate control measures can be introduced. It has been established that airborne transmission of FMD virus is one of the mechanisms by which disease is transmitted and consequently it is important to develop a capability by which this can be rapidly assessed. This often involves the integration of epidemiological data collected in the field, laboratory investigations and the use of a transport and dispersion model. Before an accurate prediction of airborne spread can be made it is of vital importance to understand each part of the disease chain together with its errors and uncertainties. How the model then represents the data is also key to obtaining a good estimate of disease spread. This paper identifies the key components in the models and identifies current errors and uncertainties. Introduction Laboratory and epidemiological studies have identified airborne transmission of FMD virus as one of the mechanisms by which disease is transmitted (reviewed by Alexandersen et al. 2003). A number of field studies have indicated that airborne virus was the most likely mechanism for disease transmission over many tens of km over land and several occasions involving a hundred or more km over the sea (Hugh-Jones and Wright 1970; Sellers and Foreman 1973; Donaldson et al. 1982; Sørensen et al. 2000; Gloster et al. 2003; Mikkelsen et al. 2003). In principle the airborne disease cycle is easy to understand (emission, transport and infection). However, in practice the inter-relation is far more complex and requires the use of a computer model to assess the potential for disease spread. For example, virus emission rates depend critically on the species involved, the stage of disease and the meteorological conditions. The meteorological conditions may vary significantly throughout one day, let alone over a typical period of virus emission (often a number of days). Definition of areas of risk is often made more difficult in areas of complex topography. The quality of an assessment of airborne transmission will only be as good as the input data and the assumptions and formulation of the model. Inaccurate model output may be misleading or wrong and any operational decisions taken on the strength of it flawed. Consequently it is important to have a detailed understanding of the quality and representivity model input data and how the model handles this information. Materials and Methods There are a number of atmospheric transport and dispersion models in use at centres around the world and potentially there are numerous combinations of inputs, outputs and degree of integration into decision support models (Sørensen 1998; Sanson, Morris and Stern 1999; Sørensen et al. 2000; Sørensen et al. 2001; Morris et al. 2002; Gloster et al. 2003). However, in general they all conform to a similar generic structure. This structure is used as the basis for assessing our current understanding and ability to predict airborne transmission of FMD. Results - Disease transmission and model representation Atmospheric transport and dispersion models are supplied with two major inputs; epidemiological to determine source terms and virus characteristics and meteorological data for virus transport. The output is typically presented in terms of virus concentration as a function of distance from the source. If a Graphical Interface System (GIS) is available other relevant data e.g. livestock distribution can also be presented simultaneously. 227
